Over the last 30 days, we shipped a meaningful expansion of the Cencori platform.

This was not one isolated launch. It was a broad push across the surfaces that matter when teams move from AI demos to production systems: gateway reliability, model access, code scanning, observability, monetization, integrations, and enterprise controls.

Here is the condensed version of what is now live.

AI Gateway: More Models, Better Routing, Stronger Reliability

We expanded the gateway in a few important directions at once.

Added support for GPT-5.3 Instant
Added support for GPT-5.4 and GPT-5.4 Pro
Added custom provider routing for teams working with non-default endpoints
Added semantic caching to chat flows
Hardened behavior so support systems like Redis-backed rate limiting and semantic cache failures do not unnecessarily take successful provider requests down

This matters because production AI traffic depends on more than raw model availability. Teams need routing flexibility, caching leverage, and failure behavior that degrades gracefully when auxiliary infrastructure has a bad day.

Related updates:

Cencori Scan: More Context, Better Recovery, Cleaner Remediation

Scan became a much stronger product over this stretch.

We shipped:

repo-aware chat
AI quality review
project brief and continuity memory
a richer investigation stream
stronger stale-run recovery
more resilient live scan behavior
persistent Diff and Create PR actions
better manual guidance when AI-generated fixes are unavailable

The practical shift is this: Scan moved closer to being a full investigation and remediation workflow instead of a thin layer over scan results.

Related update:

Introducing Cencori Scan

Observability: From Charts to Intelligence

Observability also got deeper.

We redesigned the observability surface to support:

unified HTTP traffic views across API and web
an intelligence panel for anomaly detection
platform-wide event tracking
per-project circuit breaker thresholds
a rebuilt geo visualization
stronger auditability through organization-level audit logs and export/backfill work

The goal here is simple: AI teams need to understand not only what requests happened, but where performance shifted, when risk spiked, and how traffic is behaving over time.

Billing and Monetization Controls

We also pushed further into the commercial side of running AI products.

New work in this window included:

end-user usage billing
gateway-linked usage accounting
Stripe Connect flows
invoicing support
clearer budget and cost-control positioning across product and marketing surfaces

This is a key part of the Cencori thesis. Production AI infrastructure should not just route requests and log them. It should help teams meter, govern, and monetize usage cleanly.

Integrations and Edge Workflows

Another major theme was making Cencori easier to connect to real deployment stacks.

We shipped:

a Vercel native integration flow
stronger edge integration typing and payload handling
tightened webhook provisioning and ownership boundaries
cleaner support for custom provider setups

This work is less flashy than a model launch, but it has a huge effect on adoption. Integrations are where “interesting platform” becomes “usable platform.”

Enterprise Controls and Governance

On the enterprise side, we shipped or expanded:

SSO / SAML
organization-level audit logs
audit log export and backfill support
model mappings
export surfaces for usage and admin workflows
project-level safety and operational controls including circuit breaker configuration

This is the layer buyers and platform owners care about once AI moves into real teams, real budgets, and real governance requirements.

The Throughline

The throughline across all of this work is straightforward:

Cencori is being built as the infrastructure layer for AI production.

That means:

model access without fragile glue code
scanning and remediation that teams can actually use
observability that leads to action
billing and monetization primitives
integrations that fit real deployment environments
governance controls that hold up in enterprise settings

Explore What’s Live

If you are building AI systems that need security, observability, routing, and control in one layer, this is the direction we are shipping toward.